Extended Kalman filter development in Lebesgue sampling framework with an application to Li-ion battery diagnosis and prognosis

Wuzhao Yan and Bin Zhang
Submission Type: 
Full Paper
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phmec_16_001.pdf269.36 KBJune 30, 2016 - 6:59pm

Extended Kalman filter method has been widely used in diagnosis and prognosis, navigation systems, and GPS for its advantage of simplicity and reasonable solution for nonlinear system. New particle filter based fault diagnosis and prognosis algorithms in Lebesgue sampling framework have been developed to save the limited calculation sources in embedded systems with limited calculation sources. In this Lebesgue sampling-based approach, Lebesgue states are defined on the fault dimension axis and algorithm is executed only when the measurement causes a transition from one Lebesgue state to another, or an event happens. This is a need-based fault diagnosis and prognosis (FDP) philosophy in which the algorithm is executed only when necessary, thus less calculation source is required. In order to make the algorithms more efficient and time-saving, extended Kalman filter algorithm is developed in Lebesgue sampling framework (LS-EKF). With the diagnostic philosophy of “execution only when necessary”, the proposed approach defines prognostic horizon on the fault state axis. The prognostic horizon is reduced, especially in the scenario that the fault grows slowly, the LS-EKF naturally benefits the uncertainty management and reduces the uncertainty accumulation. The new algorithm is verified with an application to the diagnosis and prognosis of the state of health of Li-ion battery. The diagnostic model for LS-EKF is the same as traditional EKF, while a new prognostic model for LS-EKF is proposed to describe the fault propagation of the time to failure of the battery. The diagnostic algorithms in LS-EKF are executed less than that in EKF with the same performance, α − λ performance metrics for EKF and LS-EKF are compared, the results show that LS-EKF is accurate and more time-efficient on long term prognosis than the traditional extended Kalman filter algorithms, which makes it a good solution for FDP in distributed application, where FDP functions are deployed on microcontrollers and embedded systems.

Publication Year: 
2016
Publication Volume: 
7
Publication Control Number: 
001
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
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